Layer 31 · Neuron 6259 · suppressed

L31N6259-1

Word quirks:

“比例”“的比例”“ proportion”“比例的”“比例为”“ ratio”“ proportions”“ Ratio”

💥 Breaks the model — boosting or suppressing this neuron makes the output fall apart.

Identity (according to the specimen itself)

The reply didn't hold together

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Nearest neighbors

Specimens with similar replies or word quirks. Similarity runs from −1 to 1; 1 is identical. These are leads, not shared mechanisms. How profiles are built.

By word quirks Profile recipe

Specimen	Similarity	Word quirks
1. L19N4943-1🕒	0.99	“ ratios”“比例”“ proportions”
2. L21N5108+1🕒	0.98	“比例”“ proportions”“比例为”
3. L22N3821-1🕒	0.98	“比例”“ proportion”“ proportions”
4. L24N5449+1🕒	0.97	“ proportion”“比例”“的比例”
5. L19N6576+1🕒	0.97	“ proportion”“按比例”“比例”
6. L19N5386+1💤	0.97	“ proportion”“ proportional”“比例”
7. L19N5635-1💤	0.97	“ proportion”“比例”“的比例”
8. L23N2264+1🕒	0.96	“比例”“ ratios”“的比例”
9. L22N1372-1💤	0.95	“比例”“ proportion”“ percentage”
10. L20N1515+1💤	0.95	“ proportion”“比例”“ proportional”

Shadow specimen, apart from the ten: L31N6259+1💥, -0.06.

Cousins · specimens sharing a neuron

No specimen with replies shares this neuron yet.

Technical details

Quick scan: measure prediction shifts on fixed reference text. Short test: generate replies to a small set of prompts. Longer test: extend to a larger set. Full test: run the full prompt suite. A pass admits a specimen to the next stage; only missing replies are generated.

Layer 31, neuron 6259, dose -1. This specimen has no prompt-suite replies yet. Its quick-scan results, when available, appear below. Its quick-scan row (KL 52.54, top-1 agreement 0.00) is outside the band that feeds the short test (KL 1 to 2.5, agreement 0.5 to 0.65), so it stays at the quick scan unless someone queues it by hand. A hand pick goes straight to the full test. See Method for the tiers.

Quick scan · prediction measurements

Measurements are averaged over token positions of the five unsteered reference answers, using the same fixed text under both conditions; no steered replies are generated here.

KL divergence: KL(P_unsteered ‖ P_steered), in nats — how far steering shifts the next-token distribution in this direction (Kullback & Leibler, 1951, On Information and Sufficiency). Unsteered ≈ 0.001.
Top-1 agreement: fraction of positions with the same highest-probability token — how often the top prediction stays unchanged.
ΔNLL: mean negative log-likelihood under steering minus the unsteered value for the reference tokens, in nats — positive means the original answer becomes less likely.
Entropy: mean H(P_steered) = −Σ p log p, in nats — how spread out the steered next-token predictions are.

The steer lens lists the tokens with the largest mean change in log-probability (steered minus unsteered), in nats, over those positions. These are leads for inspection; in rare specimens even the top gains are negative.

Setting	KL divergence	Top-1 agreement	ΔNLL	Entropy	Steer lens
L31N6259+1💥 shadow specimen t0-v0.1	5.847	0.11	6.152	10.21	“amel”“pop”“шев”“鬼”“aktur”“ Grinder”“utung”“ papan”
L31N6259-1 t0-v0.1	52.542	0.00	52.818	0.09	Top boosted tokens ↑

Method

A specimen is a fixed steering configuration of Qwen3.5-4B: one MLP neuron or a pair, each with a signed dose. Unsteered is the control. L identifies the layer; N, the neuron.

Additive activation steering. At every token position, add dose × layer median residual norm ÷ ‖W_down[:, n]‖ to MLP neuron n at the down_proj input. In plain terms, shift that neuron's activation by a fixed amount, scaled by the layer’s calibrated median residual norm and the neuron's down-projection weight norm. Positive doses push up; negative doses push down. Dose is normalized intervention size, not a percentage or probability.